Saas website building platform system integrated with aigc function and construction method thereof

Abstract

The application discloses a SaaS website building platform system integrated with AIGC function and a construction method thereof, relates to the technical field of website building, and comprises a demand analysis and architecture design module, a multi-modal AIGC engine module, a distributed computing and real-time generation module, a modular design and data security module, a user interaction and editing module, a system test and optimization module and a deployment and online module. In the application, the multi-modal AIGC engine is integrated, text, image, video, code and other multi-modal content generation are realized, and diversified requirements are met. Distributed computing and real-time generation ensure high concurrency and low latency of content generation through a distributed architecture and GPU acceleration technology. Modular design and data security adopt a micro-service architecture, support independent deployment and expansion of function modules, and guarantee data security. User interaction and intelligent recommendation provide an intuitive interaction interface and intelligent recommendation function, and improve user experience.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of website building, and in particular to an SaaS website building platform system integrated with an AIGC function and a construction method thereof. BACKGROUND

[0002] The SaaS (Software as a Service) website building platform system is a website building tool based on cloud computing. Users do not need to download or install software, but only need to access and use it through a browser. This type of platform usually provides modular functions to help users quickly build and manage websites, and is suitable for various scenarios such as enterprises, individuals and e-commerce.

[0003] According to the application publication number: CN117873456A-A modular website building management system, wherein it is recorded that "through the independent working mode, each module can work independently, even if a single module fails, it will not affect the whole system working, when each module reaches full load, the system will automatically start another module, so as to ensure that the output of the system always matches the actual demand, and ensure that each module runs efficiently and saves resources, and improves efficiency". Therefore, those skilled in the art know that each module in the reference patent works independently, and the whole system load balancing cannot be realized, which affects the efficiency of system operation; moreover, the data security of the system is not well managed and controlled, which affects the reliability of system operation.

[0004] In view of the above, an SaaS website building platform system integrated with an AIGC function and a construction method thereof are designed. SUMMARY

[0005] In order to overcome the above-mentioned deficiencies, an SaaS website building platform system integrated with an AIGC function and a construction method thereof are provided.

[0006] The application achieves the above-mentioned purposes through the following technical solutions:

[0007] An SaaS website building platform system integrated with an AIGC function, comprising

[0008] A requirement analysis and architecture design module for determining user requirements and system targets, designing a modular and scalable system architecture,

[0009] A multi-modal AIGC engine module for integrating text, image, video and code multi-modal content generation capabilities to meet diverse needs;

[0010] A distributed computing and real-time generation module for realizing high concurrency and low latency content generation through a distributed architecture and high-performance computing resources;

[0011] Modular design and data security module for adopting modular design to ensure system scalability and flexibility while ensuring data security;

[0012] User interaction and editing module for providing an intuitive interface to support real-time editing and previewing of generated content by users;

[0013] System testing and optimization module for monitoring system running state, optimizing resource utilization and user experience;

[0014] Deployment and online module for deploying the system to the production environment to ensure high availability and scalability. As a preferred embodiment, the requirement analysis and architecture design module includes user demand research module, system target definition module, system architecture design module and technology selection module, the user demand research module collects the functional requirements of potential users for the website building platform through questionnaire survey, user interview and other methods, the system target definition module determines the core targets of the platform (such as fast website building, intelligent content generation, multi-device compatibility, etc.) and formulates key performance indicators (such as generation delay ≤1 second, supporting 1000 concurrent users, etc.), the system architecture design module adopts microservices architecture to split the system into independent functional modules (such as AIGC engine, user interaction, data security, etc.) and design communication protocols between modules (such as RESTAPI, gRPC), the technology selection module selects appropriate technology stack (such as React front-end, Node.js back-end, Kubernetes deployment) and determines the integration method of AIGC model (such as OpenAI API, HuggingFace model). As a preferred embodiment, the multi-modal AIGC engine module includes text generation module, image generation module, video generation module, code generation module and multi-modal fusion algorithm module, the text generation module generates high-quality natural language text such as website copywriting and SEO optimized content, the image generation module generates high-quality images such as website pictures, icons and backgrounds, the video generation module generates high-quality videos such as promotional videos and dynamic content, the code generation module generates high-quality front-end code or functional plug-ins such as HTML, CSS and JavaScript, and the multi-modal fusion algorithm module fuses and optimizes content of different modalities such as text and image to generate picture-text content.

[0015] As a preferred embodiment, the distributed computing and real-time generation module includes task scheduling module, GPU acceleration module and edge computing module, the task scheduling module dynamically allocates computing resources to ensure efficient execution of generation tasks, the GPU acceleration module uses GPU cluster to accelerate content generation and reduce latency, and the edge computing module processes user requests locally through edge nodes to improve response speed.

[0016] As preferred, the modular design and data security module includes a modular architecture module, a data encryption module, and an access control module. The modular architecture module is based on a microservices architecture, supporting independent deployment and expansion of functional modules. The data encryption module is used for encrypted storage and transmission of user data. The access control module manages user access based on roles and permissions, ensuring data privacy.

[0017] As preferred, the user interaction and editing module includes a drag-and-drop editor module, a real-time preview module, and an intelligent recommendation module. The drag-and-drop editor module supports users to adjust layout and content through drag-and-drop. The real-time preview module generates content instant rendering, supporting multi-device adaptation. The intelligent recommendation module recommends templates, content, or functional modules according to user needs.

[0018] As preferred, the system testing and optimization module includes a performance monitoring module, a user feedback module, and a data analysis module. The performance monitoring module monitors system performance in real time, discovers and solves problems in a timely manner. The user feedback module collects user feedback, drives functional optimization and iteration. The data analysis module analyzes user behavior and data, optimizing generation models and recommendation algorithms.

[0019] As preferred, the deployment and online module includes a cloud platform deployment module, an online preparation module, and a user support and maintenance module. The cloud platform deployment module deploys the system to cloud platforms (such as AWS, Azure), uses load balancing and automatic expansion functions, and configures monitoring tools (such as Prometheus, Grafana) to monitor system running status in real time. The online preparation module performs final testing to ensure system stability and performance, and formulates online plans to ensure smooth transition. The user support and maintenance module provides user documentation and training to help users quickly get started, and establishes an operation and maintenance team to timely solve user problems and system failures.

[0020] A construction method of a SaaS website building platform integrating AIGC functions as described above, comprising the following steps

[0021] S1, requirement analysis and architecture design;

[0022] S2, multi-modal AIGC engine integration;

[0023] S3, distributed computing and real-time generation implementation;

[0024] S4, modular design and data security implementation;

[0025] S5, user interaction and editing module development;

[0026] S6, system testing and optimization;

[0027] S7, deployment and online.

[0028] As preferred, the requirement analysis and architecture design include the following steps:

[0029] S11, user requirement investigation, through questionnaire survey, user interview, etc., to collect potential users' functional requirements of the website building platform (such as multi-modal content generation, real-time editing, SEO optimization, etc.) and analyze the functions of competitors, and clarify the differentiated advantages;

[0030] S12, system target definition, determine the core target of the platform (such as fast website building, intelligent content generation, multi-device compatibility, etc.) and formulate key performance indicators (such as generation delay ≤1 second, support 1000 concurrent users, etc.);

[0031] S13, system architecture design, adopt micro-service architecture, split the system into independent functional modules (such as AIGC engine, user interaction, data security, etc.) and design inter-module communication protocol (such as RESTAPI, gRPC);

[0032] S14, technology selection, select appropriate technology stack (such as React front-end, Node.js back-end, Kubernetes deployment) and determine the integration method of AIGC model (such as OpenAI API, HuggingFace model);

[0033] The multi-modal AIGC engine integration includes the following steps:

[0034] S21, model selection and evaluation, select suitable multi-modal model (such as GPT-4 text generation, DALL·E image generation, Runway video generation) and evaluate the performance of the model (such as generation quality, response speed, resource consumption);

[0035] S22, API integration and encapsulation, integrate the selected model through API call, encapsulate as a unified interface layer, and realize multi-modal switching function, support user to select generation type (such as text, image, video);

[0036] S23, model optimization and fine-tuning, according to user feedback and generation results, fine-tune the model (such as transfer learning, incremental learning), and optimize model parameters (such as temperature, resolution, frame rate) to improve generation quality;

[0037] S24, performance test, conduct high-concurrency test to ensure the stability of the model in multi-user scenario, and optimize model call frequency and resource allocation to reduce delay and cost;

[0038] The distributed computing and real-time generation implementation includes the following steps:

[0039] S31, distributed architecture building, deploying Kubernetes cluster, supporting dynamic scheduling and expansion of computing resources and configuring GPU nodes to accelerate content generation tasks;

[0040] S32, task scheduling and load balancing, implementing task scheduling algorithms (such as RoundRobin, Min-Min), ensuring load balancing, and monitoring node load to dynamically adjust task allocation;

[0041] S33, edge computing node deployment, deploying edge nodes near users' geographic locations to reduce generation delay and implementing task migration strategies to select the best computing node according to the delay threshold;

[0042] S34, real-time rendering and interaction, integrating real-time rendering technologies (such as WebGL, Three.js) to support real-time preview of generated content and implementing interactive editing functions, allowing users to make adjustments during the generation process;

[0043] The modular design and data security implementation includes the following steps:

[0044] S41, modular architecture implementation, splitting the system into independent functional modules (such as AIGC engine, user management, data security), and using containerization technology (such as Docker) to deploy each module to ensure independence and scalability;

[0045] S42, data encryption and storage, implementing data encryption algorithms (such as AES-256) to encrypt user data for storage and transmission, and configuring secure storage solutions (such as AWSS3, AzureBlobStorage);

[0046] S43, access control and permission management, implementing role-based access control (RBAC) to manage user permissions (such as administrators, regular users), and configuring access policies to ensure the security of sensitive data;

[0047] S44, security monitoring and response, deploying security monitoring tools (such as IDS, SIEM) to detect system anomalies in real time and developing emergency response strategies to respond to security threats in a timely manner;

[0048] The user interaction and editing module development includes the following steps:

[0049] S51, interactive interface design, designing an intuitive user interface that supports drag-and-drop operations and real-time preview, and implementing multi-device adaptation to ensure compatibility on PCs, mobile phones, and tablets;

[0050] S52, the intelligent recommendation function is realized, based on collaborative filtering and knowledge graph technology, the intelligent recommendation function is realized, and related templates, contents or function modules are recommended according to user demand;

[0051] S53, the real-time editing function is developed, the real-time rendering and editing functions are realized, the user can adjust the generated content, and the multi-language support is integrated to meet the internationalization demand;

[0052] S54, user experience optimization, collect user feedback, optimize interface design and interaction process, and carry out A / B test to select the best design scheme;

[0053] The system test and optimization includes the following steps:

[0054] S61, function test, function test is carried out for each module to ensure that the demand is met, and the problems found in the test are repaired, and the system function is optimized;

[0055] S62, performance test, high concurrency test is carried out, the performance of the system under stress is evaluated, and resource allocation and task scheduling are optimized to improve system performance;

[0056] S63, user test, invite target users to test, collect feedback, and optimize the generation quality and interaction experience according to user feedback;

[0057] S64, continuous optimization, establish continuous integration and continuous delivery (CI / CD) process, support rapid iteration, and continuously optimize AIGC model and system function according to user data and generation result;

[0058] The deployment and online include the following steps:

[0059] S71, cloud platform deployment, deploy the system to the cloud platform (such as AWS, Azure), use load balancing and automatic expansion function, and configure monitoring tools (such as Prometheus, Grafana) to monitor the running state of the system in real time;

[0060] S72, online preparation, final test is carried out to ensure system stability and performance, and online plan is made to ensure smooth transition;

[0061] S73, user support and maintenance, provide user documents and training to help users quickly start, and establish operation and maintenance team to solve user problems and system failures in time.

[0062] The SaaS website building platform system and the construction method of the integrated AIGC function have the advantages that,

[0063] 1. Multi-modal AIGC engine integration, realizing multi-modal content generation such as text, image, video, and code, to meet diversified needs;

[0064] 2. Distributed computing and real-time generation, ensuring high concurrency and low latency content generation through distributed architecture and GPU acceleration technology;

[0065] 3. Modular design and data security, adopting micro-service architecture to support independent deployment and expansion of functional modules while ensuring data security;

[0066] 4. User interaction and intelligent recommendation, providing intuitive interface and intelligent recommendation functions to improve user experience;

[0067] 5. Data security and privacy protection, ensuring the security and privacy of user data, and complying with relevant regulations. BRIEF DESCRIPTION OF DRAWINGS

[0068] The present application will be described by way of example and with reference to the accompanying drawings, in which:

[0069] Figure 1 is a step diagram of the construction method of the present application;

[0070] Figure 2 is a step diagram of the requirement analysis and architecture design of the present application;

[0071] Figure 3 is a step diagram of the multi-modal AIGC engine integration of the present application;

[0072] Figure 4 is a step diagram of the distributed computing and real-time generation implementation of the present application;

[0073] Figure 5 is a step diagram of the modular design and data security implementation of the present application;

[0074] Figure 6 is a step diagram of the user interaction and editing module development of the present application;

[0075] Figure 7 is a step diagram of the system testing and optimization of the present application;

[0076] Figure 8 is a step diagram of the deployment and online of the present application. DETAILED DESCRIPTION

[0077] The present application will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams that only schematically illustrate the basic structure of the present application, and thus only show the components related to the present application.

[0078] As Figures 1-8As shown, a SaaS website building platform system integrated with AIGC functions includes

[0079] A requirement analysis and architecture design module is used to clarify user requirements and system goals, design a modular and scalable system architecture,

[0080] A multi-modal AIGC engine module is used to integrate text, image, video, and code multi-modal content generation capabilities to meet diverse needs;

[0081] A distributed computing and real-time generation module is used to achieve high concurrency and low latency content generation through distributed architecture and high-performance computing resources;

[0082] A modular design and data security module is used to adopt modular design to ensure system scalability and flexibility while ensuring data security;

[0083] A user interaction and editing module is used to provide an intuitive interface that supports real-time editing and preview of generated content by users;

[0084] A system testing and optimization module is used to monitor system operation, optimize resource utilization, and improve user experience;

[0085] The deployment and online module is used to deploy the system to the production environment, ensuring high availability and scalability. Specifically, the requirement analysis and architecture design module includes user demand investigation module, system target definition module, system architecture design module and technology selection module. The user demand investigation module collects the functional requirements of potential users for the website building platform through questionnaire survey, user interview and other methods. The system target definition module determines the core targets of the platform (such as fast website building, intelligent content generation, multi-device compatibility, etc.) and formulates key performance indicators (such as generation delay ≤1 second, supporting 1000 concurrent users, etc.). The system architecture design module adopts micro-service architecture to split the system into independent functional modules (such as AIGC engine, user interaction, data security, etc.) and design the communication protocol between modules (such as RESTAPI, gRPC). The technology selection module selects appropriate technology stack (such as React front-end, Node.js back-end, Kubernetes deployment) and determines the integration method of AIGC model (such as OpenAI API, HuggingFace model). Specifically, the multi-modal AIGC engine module includes text generation module, image generation module, video generation module, code generation module and multi-modal fusion algorithm module. The text generation module generates high-quality natural language text, such as website copywriting, SEO optimized content. The image generation module generates high-quality images, such as website pictures, icons, backgrounds. The video generation module generates high-quality videos, such as promotional videos, dynamic content. The code generation module generates high-quality front-end code or functional plug-ins, such as HTML, CSS, JavaScript, etc. The multi-modal fusion algorithm module fuses and optimizes content of different modalities, such as combining text and images to generate picture-text content.

[0086] Specifically, the distributed computing and real-time generation module includes task scheduling module, GPU acceleration module and edge computing module. The task scheduling module dynamically allocates computing resources to ensure efficient execution of generation tasks. The GPU acceleration module uses GPU cluster to accelerate content generation and reduce latency. The edge computing module processes user requests locally through edge nodes to improve response speed.

[0087] Specifically, the modular design and data security module includes modular architecture module, data encryption module and access control module. The modular architecture module is based on micro-service architecture, supporting independent deployment and extension of functional modules. The data encryption module encrypts user data for storage and transmission. The access control module manages user access based on roles and permissions to ensure data privacy.

[0088] Specifically, the user interaction and editing module includes a drag-and-drop editor module, a real-time preview module, and an intelligent recommendation module. The drag-and-drop editor module is used to support users to adjust the layout and content through a drag-and-drop method. The real-time preview module is used to generate content instant rendering and support multi-device adaptation. The intelligent recommendation module is used to recommend templates, content, or functional modules according to user needs.

[0089] Specifically, the system testing and optimization module includes a performance monitoring module, a user feedback module, and a data analysis module. The performance monitoring module is used to monitor system performance in real time, discover and solve problems in a timely manner. The user feedback module is used to collect user feedback, drive function optimization and iteration. The data analysis module is used to analyze user behavior and data, optimize generation models and recommendation algorithms.

[0090] Specifically, the deployment and online module includes a cloud platform deployment module, an online preparation module, and a user support and maintenance module. The cloud platform deployment module is used to deploy the system to the cloud platform (such as AWS, Azure), use load balancing and automatic expansion functions, and configure monitoring tools (such as Prometheus, Grafana) to monitor system running status in real time. The online preparation module is used for final testing to ensure system stability and performance and to develop an online plan to ensure smooth transition. The user support and maintenance module is used to provide user documentation and training to help users quickly get started, and to establish an operation and maintenance team to timely solve user problems and system failures.

[0091] A construction method of a SaaS website building platform integrated with AIGC functions as described above, comprising the following steps

[0092] S1, requirement analysis and architecture design;

[0093] S2, multi-modal AIGC engine integration;

[0094] S3, distributed computing and real-time generation implementation;

[0095] S4, modular design and data security implementation;

[0096] S5, user interaction and editing module development;

[0097] S6, system testing and optimization;

[0098] S7, deployment and online.

[0099] Specifically, the requirement analysis and architecture design includes the following steps:

[0100] S11, user demand research, through questionnaire survey, user interview and other ways, collect potential users' functional requirements of the platform (such as multi-modal content generation, real-time editing, SEO optimization, etc.) and analyze the functions of the competitors, and clarify the differentiated advantages;

[0101] S12, system target definition, determine the core target of the platform (such as fast website building, intelligent content generation, multi-device compatibility, etc.) and formulate key performance indicators (such as generation delay ≤1 second, support 1000 concurrent users, etc.);

[0102] S13, system architecture design, adopt micro-service architecture, split the system into independent functional modules (such as AIGC engine, user interaction, data security, etc.) and design inter-module communication protocol (such as RESTAPI, gRPC);

[0103] S14, technology selection, select appropriate technology stack (such as React front-end, Node.js back-end, Kubernetes deployment) and determine the integration method of AIGC model (such as OpenAI API, HuggingFace model);

[0104] The multi-modal AIGC engine integration includes the following steps:

[0105] S21, model selection and evaluation, select suitable multi-modal model (such as GPT-4 text generation, DALL·E image generation, Runway video generation) and evaluate the performance of the model (such as generation quality, response speed, resource consumption);

[0106] S22, API integration and encapsulation, integrate the selected model through API call, encapsulate as a unified interface layer, and realize multi-modal switching function, support user to select generation type (such as text, image, video);

[0107] S23, model optimization and fine-tuning, according to user feedback and generation results, fine-tune the model (such as transfer learning, incremental learning), and optimize model parameters (such as temperature, resolution, frame rate) to improve generation quality;

[0108] S24, performance test, conduct high-concurrency test, ensure the stability of the model in multi-user scenario, and optimize model call frequency and resource allocation, reduce delay and cost;

[0109] The distributed computing and real-time generation implementation includes the following steps:

[0110] S31, distributed architecture building, deploy Kubernetes cluster, support dynamic scheduling and expansion of computing resources and configure GPU nodes to accelerate content generation tasks;

[0111] S32, task scheduling and load balancing, implement task scheduling algorithms (such as RoundRobin, Min-Min), ensure load balancing, and monitor node load, dynamically adjust task allocation;

[0112] S33, edge computing node deployment, deploy edge nodes near the user's geographic location, reduce generation delay, and implement task migration strategy, select the best computing node according to the delay threshold;

[0113] S34, real-time rendering and interaction, integrate real-time rendering technology (such as WebGL, Three.js), support real-time preview of generated content, and implement interactive editing functions, users can adjust during generation;

[0114] The modular design and data security implementation includes the following steps:

[0115] S41, modular architecture implementation, split the system into independent functional modules (such as AIGC engine, user management, data security), and use containerization technology (such as Docker) to deploy each module, ensuring independence and scalability;

[0116] S42, data encryption and storage, implement data encryption algorithms (such as AES-256), encrypt user data for storage and transmission, and configure secure storage solutions (such as AWSS3, AzureBlobStorage);

[0117] S43, access control and permission management, implement role-based access control (RBAC), manage user permissions (such as administrators, ordinary users), and configure access policies to ensure the security of sensitive data;

[0118] S44, security monitoring and response, deploy security monitoring tools (such as IDS, SIEM), detect system anomalies in real time, and develop emergency response strategies to respond to security threats in a timely manner;

[0119] The user interaction and editing module development includes the following steps:

[0120] S51, interactive interface design, design an intuitive user interface that supports drag-and-drop operations and real-time preview, and implement multi-device adaptation to ensure compatibility on PCs, mobile phones, and tablets;

[0121] S52, intelligent recommendation function implementation, based on collaborative filtering and knowledge graph technology, implement intelligent recommendation functions, and recommend related templates, content or functional modules according to user needs;

[0122] S53, Real-time editing function development, realize real-time rendering and editing function, support users to adjust the generated content, and integrate multi-language support to meet the internationalization needs;

[0123] S54, User experience optimization, collect user feedback, optimize interface design and interaction process, and conduct A / B testing to select the best design solution;

[0124] The system testing and optimization includes the following steps:

[0125] S61, Function testing, test each module to ensure compliance with requirements and fix problems found during testing to optimize system functions;

[0126] S62, Performance testing, conduct high-concurrency testing to evaluate system performance under stress, and optimize resource allocation and task scheduling to improve system performance;

[0127] S63, User testing, invite target users to test, collect feedback, and optimize generation quality and interaction experience based on user feedback;

[0128] S64, Continuous optimization, establish continuous integration and continuous delivery (CI / CD) processes to support rapid iteration, and continuously optimize AIGC models and system functions based on user data and generation results;

[0129] The deployment and online include the following steps:

[0130] S71, Cloud platform deployment, deploy the system to a cloud platform (such as AWS, Azure), use load balancing and automatic scaling functions, and configure monitoring tools (such as Prometheus, Grafana) to monitor system running status in real time;

[0131] S72, Online preparation, conduct final testing to ensure system stability and performance, and develop an online plan to ensure smooth transition;

[0132] S73, User support and maintenance, provide user documentation and training to help users quickly get started, and establish an operations team to promptly address user issues and system failures.

[0133] Embodiment one: Enterprise website building

[0134] 1. Demand analysis and architecture design:

[0135] - User requirements: The enterprise needs to display company information, products and services, and support multiple languages.

[0136] - System goals: Quickly generate an enterprise website and support SEO optimization.

[0137] - Technology selection: Choose React frontend, Node.js backend, Kubernetes deployment.

[0138] 2. Multi-modal AIGC engine integration:

[0139] - Model selection: Integrate GPT-4 for generating text and DALL·E for generating images.

[0140] - API encapsulation: Implement a unified interface to support user selection of generation types.

[0141] 3. Distributed computing and real-time generation:

[0142] - Architecture building: Deploy a Kubernetes cluster and configure GPU nodes.

[0143] - Task scheduling: Implement the RoundRobin algorithm to ensure load balancing.

[0144] 4. Modular design and data security:

[0145] - Modular architecture: Split into user management, AIGC engine, and data security modules.

[0146] - Data encryption: Use AES-256 to encrypt user data storage and transmission.

[0147] 5. User interaction and editing:

[0148] - Interactive interface: Design a drag-and-drop editor with real-time preview.

[0149] - Intelligent recommendations: Recommend relevant templates and content based on enterprise needs.

[0150] 6. System testing and optimization:

[0151] - Function testing: Test text generation, image generation, and other functions.

[0152] - Performance testing: Conduct high-concurrency testing and optimize resource allocation.

[0153] 7. Deployment and go-live:

[0154] - Cloud platform deployment: Deploy the system on AWS and configure load balancing.

[0155] - User support: Provide enterprise user documentation and training.

[0156] Example Two: E-commerce Platform Website Building

[0157] 1. Requirement analysis and architecture design:

[0158] - User requirements: The e-commerce platform needs to support product management, order processing, and payment functions.

[0159] - System goal: Quickly generate e-commerce websites, support multiple payment methods.

[0160] - Technology selection: Choose Vue front-end, SpringBoot back-end, Docker deployment.

[0161] 2. Multi-modal AIGC engine integration:

[0162] - Model selection: Integrate GPT-4 to generate product descriptions and Stable Diffusion to generate product images. - API encapsulation: Implement a unified interface to support user selection of generation types.

[0163] 3. Distributed computing and real-time generation:

[0164] - Architecture building: Deploy Kubernetes clusters and configure GPU nodes.

[0165] - Task scheduling: Implement the Min-Min algorithm to ensure load balancing.

[0166] 4. Modular design and data security:

[0167] - Modular architecture: Split into product management, order processing, and payment modules.

[0168] - Data encryption: Use RSA encryption to store and transmit user data.

[0169] 5. User interaction and editing:

[0170] - Interactive interface: Design a drag-and-drop editor with real-time preview.

[0171] - Intelligent recommendation: Recommend relevant templates and content based on product categories.

[0172] 6. System testing and optimization:

[0173] - Function testing: Test product description generation, order processing, and other functions.

[0174] - Performance testing: Conduct high-concurrency testing and optimize resource allocation.

[0175] 7. Deployment and online:

[0176] - Cloud platform deployment: Deploy the system to Azure and configure load balancing.

[0177] - User support: Provide e-commerce user documentation and training.

[0178] Example Three: Personal Blog Website Building

[0179] 1. Requirement analysis and architecture design:

[0180] - User needs: Personal needs to showcase blog posts, support SEO optimization.

[0181] - System goals: Quickly generate personal blogs, support multi-device compatibility.

[0182] - Technology selection: Choose Angular frontend, Django backend, Kubernetes deployment.

[0183] 2. Multi-modal AIGC engine integration:

[0184] - Model selection: Integrate GPT-4 to generate blog posts and DALL·E to generate cover images.

[0185] - API encapsulation: Implement a unified interface to support user selection of generation types.

[0186] 3. Distributed computing and real-time generation:

[0187] - Architecture building: Deploy Kubernetes clusters and configure GPU nodes.

[0188] - Task scheduling: Implement RoundRobin algorithm to ensure load balancing.

[0189] 4. Modular design and data security:

[0190] - Modular architecture: Split into user management, AIGC engine, and data security modules.

[0191] - Data encryption: Use AES-256 to encrypt user data storage and transmission.

[0192] 5. User interaction and editing:

[0193] - Interactive interface: Design a drag-and-drop editor with real-time preview support.

[0194] - Intelligent recommendations: Recommend relevant templates and content based on blog themes.

[0195] 6. System testing and optimization:

[0196] - Function testing: Test blog post generation, cover image generation, and other functions.

[0197] - Performance testing: Conduct high-concurrency testing and optimize resource allocation.

[0198] 7. Deployment and online:

[0199] - Cloud platform deployment: Deploy the system on Google Cloud and configure load balancing.

[0200] - User Support: Providing personal user documentation and training.

[0201] In summary, the patent has the following advantages:

[0202] 1. Model optimization, continuously optimize AIGC model according to user feedback and generation results, support more modal generation.

[0203] 2. Performance improvement, optimize distributed computing architecture, reduce generation delay; introduce more efficient GPU acceleration technology.

[0204] 3. User experience, enhance interactive editing function, support more custom options; provide more intelligent recommendation algorithm, improve user satisfaction.

[0205] 4. Data security, introduce blockchain technology to ensure content copyright and data security; strengthen access control and permission management to prevent data leakage.

[0206] 5. Ecological expansion, open API interface, support third-party developers to access AIGC function; build developer community, enrich platform ecology.

[0207] Based on the above invention, through the above description, relevant staff can make various changes and modifications without deviating from the scope of the technical idea of the invention. The technical scope of the invention is not limited to the content in the specification, and the technical scope must be determined according to the scope of the claims.

Claims

1. A SaaS website building platform system integrating AIGC functionality, characterized in that: include The requirements analysis and architecture design module is used to clarify user needs and system goals, and to design a modular and scalable system architecture. The multimodal AIGC engine module integrates the ability to generate multimodal content, including text, images, videos, and code, to meet diverse needs. The distributed computing and real-time generation module is used to achieve high-concurrency, low-latency content generation through a distributed architecture and high-performance computing resources; Modular design and data security module: This module is designed to ensure system scalability and flexibility while protecting data security through modular design. The user interaction and editing module provides an intuitive interactive interface that allows users to edit and preview generated content in real time. The system testing and optimization module is used to monitor the system's operating status and optimize resource utilization and user experience. The deployment and launch module is used to deploy the system to the production environment to ensure high availability and scalability; The modular design and data security module includes a modular architecture module, a data encryption module, and an access control module. The modular architecture module is based on a microservice architecture and supports the independent deployment and expansion of functional modules. The data encryption module is used to encrypt, store, and transmit user data. The access control module manages user access based on roles and permissions to ensure data privacy. The user interaction and editing module includes a drag-and-drop editor module, a real-time preview module, and an intelligent recommendation module. The drag-and-drop editor module allows users to adjust the layout and content by dragging and dropping. The real-time preview module generates content and renders it instantly, supporting multi-device adaptation. The intelligent recommendation module recommends templates, content, or functional modules based on user needs. The deployment and launch module includes a cloud platform deployment module, a launch preparation module, and a user support and maintenance module. The cloud platform deployment module is used to deploy the system to the cloud platform, utilize load balancing and automatic scaling functions, and configure monitoring tools to monitor the system's operating status in real time. The launch preparation module is used to conduct final testing to ensure system stability and performance, and to develop a launch plan to ensure a smooth transition. The user support and maintenance module is used to provide user documentation and training to help users get started quickly, and to establish an operations and maintenance team to resolve user issues and system failures in a timely manner.

2. The SaaS website building platform system integrating AIGC function according to claim 1, characterized in that: The requirements analysis and architecture design module includes a user requirements survey module, a system goal definition module, a system architecture design module, and a technology selection module. The user requirements survey module is used to collect potential users' functional requirements for the website building platform through questionnaires and user interviews. The system goal definition module is used to determine the core goals of the platform and formulate key performance indicators. The system architecture design module is used to adopt a microservice architecture, decompose the system into independent functional modules and communication protocols between design modules. The technology selection module is used to select a suitable technology stack and determine the integration method of the AIGC model.

3. The SaaS website building platform system integrating AIGC functionality according to claim 1, characterized in that: The multimodal AIGC engine module includes a text generation module, an image generation module, a video generation module, a code generation module, and a multimodal fusion algorithm module. The text generation module is used to generate high-quality natural language text, the image generation module is used to generate high-quality images, the video generation module is used to generate high-quality videos, the code generation module is used to generate high-quality front-end code or functional plugins, and the multimodal fusion algorithm module is used to fuse and optimize content from different modalities.

4. The SaaS website building platform system integrating AIGC function according to claim 1, characterized in that: The distributed computing and real-time generation module includes a task scheduling module, a GPU acceleration module, and an edge computing module. The task scheduling module is used to dynamically allocate computing resources to ensure efficient execution of generation tasks. The GPU acceleration module is used to accelerate content generation using GPU clusters and reduce latency. The edge computing module is used to process user requests locally through edge nodes to improve response speed.

5. The SaaS website building platform system integrating AIGC function according to claim 1, characterized in that: The system testing and optimization module includes a performance monitoring module, a user feedback module, and a data analysis module. The performance monitoring module is used to monitor system performance in real time, identify and resolve problems promptly, the user feedback module is used to collect user feedback to drive function optimization and iteration, and the data analysis module is used to analyze user behavior and data to optimize the generation model and recommendation algorithm.

6. A method for constructing a SaaS website building platform integrating AIGC functionality according to any one of claims 1-5, characterized in that: Includes the following steps S1. Requirements Analysis and Architecture Design; S2, multimodal AIGC engine integration; S3, distributed computing and real-time generation implementation; S4, Modular Design and Data Security Implementation; S5, user interaction and editing module development; S6. System testing and optimization; S7, Deployment and Launch.

7. The method for constructing a SaaS website building platform integrating AIGC functionality according to claim 6, characterized in that: The requirements analysis and architecture design include the following steps: S11. User needs research: Collect potential users' functional needs for the website building platform and analyze competitor functions through questionnaires and user interviews to clarify the differentiated advantages. S12. System objective definition: Determine the core objectives of the platform and formulate key performance indicators; S13. System architecture design: adopt a microservice architecture, and decompose the system into independent functional modules and communication protocols between design modules; S14. Technology selection: Choose a suitable technology stack and determine the integration method of the AIGC model; The multimodal AIGC engine integration includes the following steps: S21. Model selection and evaluation: Select a suitable multimodal model and evaluate the model's performance. S22, API integration and encapsulation: Integrate the selected model through API calls, encapsulate it into a unified interface layer, and implement multimodal switching functionality, supporting users to select the generation type; S23. Model optimization and fine-tuning: Based on user feedback and generated results, fine-tune the model and optimize model parameters to improve generation quality. S24. Performance testing: Conduct high-concurrency testing to ensure the stability of the model in multi-user scenarios, and optimize the model call frequency and resource allocation to reduce latency and cost. The distributed computing and real-time generation implementation includes the following steps: S31. Distributed architecture setup, deployment of Kubernetes cluster, support for dynamic scheduling and expansion of computing resources and configuration of GPU nodes to accelerate content generation tasks; S32. Task scheduling and load balancing: Implement task scheduling algorithms, ensure load balancing, monitor node load, and dynamically adjust task allocation; S33. Edge computing node deployment: Deploy edge nodes in geographical locations close to users to reduce generation latency and implement task migration strategies, selecting the best computing node based on latency thresholds; S34. Real-time rendering and interaction: Integrates real-time rendering technology, supports real-time preview of generated content, and implements interactive editing functions, allowing users to make adjustments during the generation process; The modular design and data security implementation include the following steps: S41, modular architecture implementation, breaks down the system into independent functional modules and uses containerization technology to deploy each module, ensuring independence and scalability; S42. Data encryption and storage: Implement data encryption algorithms to encrypt, store, and transmit user data, and configure a secure storage scheme. S43. Access control and permission management: Implement role-based access control, manage user permissions, and configure access policies to ensure the security of sensitive data. S44. Security Monitoring and Response: Deploy security monitoring tools to detect system anomalies in real time and develop emergency response strategies to deal with security threats in a timely manner. The development of the user interaction and editing module includes the following steps: S51. Interactive interface design: Design an intuitive user interface that supports drag-and-drop operation and real-time preview, and achieves multi-device adaptation to ensure compatibility on PCs, mobile phones, and tablets. S52. Intelligent recommendation function implementation: Based on collaborative filtering and knowledge graph technology, intelligent recommendation function is implemented, and relevant templates, content or functional modules are recommended according to user needs. S53. Real-time editing function development, realizing real-time rendering and editing functions, supporting users to adjust the generated content, and integrating multi-language support to meet internationalization needs; S54. User experience optimization: collect user feedback, optimize interface design and interaction flow, and conduct A / B testing to select the best design solution. The system testing and optimization includes the following steps: S61. Functional testing: Perform functional testing on each module to ensure it meets the requirements, fix any issues found during testing, and optimize system functionality. S62. Performance testing: Conduct high-concurrency tests to evaluate the system's performance under stress, and optimize resource allocation and task scheduling to improve system performance; S63. User testing: Invite target users to conduct tests, collect feedback, and optimize the generation quality and interactive experience based on user feedback; S64. Continuous optimization: Establish continuous integration and continuous delivery (CI / CD) processes to support rapid iteration, and continuously optimize AIGC models and system functions based on user data and generated results. The deployment and launch include the following steps: S71, Cloud Platform Deployment: Deploy the system to the cloud platform, utilize load balancing and automatic scaling functions, and configure monitoring tools to monitor the system's operating status in real time; S72. Go-live preparation: Conduct final testing to ensure system stability and performance, and develop a go-live plan to ensure a smooth transition. S73. User support and maintenance: Provide user documentation and training to help users get started quickly, and establish an operations and maintenance team to resolve user issues and system failures in a timely manner.

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